Performs spatial Bayesian GLM for fMRI task activation
BayesGLM(
data,
vertices = NULL,
faces = NULL,
mesh = NULL,
mask = NULL,
task_names = NULL,
session_names = NULL,
combine_sessions = TRUE,
scale_BOLD = c("auto", "mean", "sd", "none"),
scale_design = TRUE,
Bayes = TRUE,
ar_order = 6,
ar_smooth = 5,
aic = FALSE,
num.threads = 4,
return_INLA = c("trimmed", "full", "minimal"),
verbose = 1,
meanTol = 1e-06,
varTol = 1e-06
)A "BayesGLM" object: a list with elements
The full result of the call to INLA::inla.
The task coefficients for the Bayesian model.
Results from the classical model: task estimates, task standard error estimates, residuals, degrees of freedom, and the mask.
The model mesh including only the locations analyzed, i.e. within mask, without missing values, and meeting meanTol and varTol.
The original mesh provided.
A mask of mesh_orig indicating the locations inside mesh.
The design matrix, after centering and scaling, but before any nuisance regression or prewhitening.
The names of the tasks.
The names of the sessions.
Hyperparameter posterior densities.
Theta estimates from the Bayesian model.
For joint group modeling.
For joint group modeling.
For joint group modeling.
For joint group modeling: The BOLD data after any centering, scaling, nuisance regression, or prewhitening.
For joint group modeling: The design matrix after any centering, scaling, nuisance regression, or prewhitening.
Vectors of values across locations: phi (AR coefficients averaged across sessions), sigma_sq (residual variance averaged across sessions), and AIC (the maximum across sessions).
match.call() for this function call.
A list of sessions in the "BfMRI.sess" object format. Each
session is a list with elements "BOLD", "design", and
optionally "nuisance". Each element should be a numeric matrix with
\(T\) rows. The name of each element in data is the name of that
session. See ?is.BfMRI.sess for details.
Note that the argument session_names can be used instead of providing
the session names as the names of the elements in data.
If Bayes, the geometry data can be provided
with either both the vertices and faces arguments, or with the
mesh argument.
vertices is a \(V \times 3\) matrix, where each row contains the
Euclidean coordinates at which a given vertex in the mesh is located.
\(V\) is the number of vertices in the mesh.
faces is a \(F \times 3\) matrix, where each row contains the
vertex indices for a given triangular face in the mesh. \(F\) is the
number of faces in the mesh.
If Bayes, the geometry data can be provided
with either both the vertices and faces arguments, or with the
mesh argument.
mesh is an "inla.mesh" object. This can be created for surface
data using make_mesh.
(Optional) A length \(V\) logical vector indicating the vertices to include.
(Optional) Names of tasks represented in design matrix.
(Optional, and only relevant for multi-session modeling)
Names of each session. Default: NULL. In BayesGLM this
argument will overwrite the names of the list entries in data, if
both exist.
If multiple sessions are provided, should their data be combined and analyzed as a single session?
If TRUE (default), the multiple sessions will be concatenated along
time after scaling and nuisance regression, but before prewhitening. If
FALSE, each session will be analyzed separately, except that a single
estimate of the AR model coefficients for prewhitening is used, estimated
across all sessions.
Option for scaling the BOLD response.
"auto" (default) will use "mean" scaling except if demeaned
data is detected (if any mean is less than one), in which case "sd"
scaling will be used instead.
"mean" scaling will scale the data to percent local signal change.
"sd" scaling will scale the data by local standard deviation.
"none" will only center the data, not scale it.
Scale the design matrix by dividing each column by its
maximum and then subtracting the mean? Default: TRUE. If
FALSE, the design matrix is centered but not scaled.
If TRUE (default), will fit a spatial Bayesian GLM in
addition to the classical GLM. (The classical GLM is always returned.)
(numeric) Controls prewhitening. If greater than zero, this
should be a number indicating the order of the autoregressive model to use
for prewhitening. If zero, do not prewhiten. Default: 6. For
multi-session models, note that a single AR model is used; the parameters
are estimated by averaging the estimates from each session.
(numeric) FWHM parameter for smoothing the AR model
coefficient estimates for prewhitening. Remember that
\(\sigma = \frac{FWHM}{2*sqrt(2*log(2)}\). Set to 0 or NULL
to not do any smoothing. Default: 5.
Use the AIC to select AR model order between 0 and
ar_order? Default: FALSE.
The maximum number of threads to use for parallel
computations: prewhitening parameter estimation, and the inla-program model
estimation. Default: 4. Note that parallel prewhitening requires the
parallel package.
Return the INLA model object? (It can be large.) Use
"trimmed" (default) to return only the more relevant results, which
is enough for both id_activations and BayesGLM2,
"minimal" to return just enough for BayesGLM2 but not
id_activations, or "full" to return the full output of
inla.
Should updates be printed? Use 1 (default) for
occasional updates, 2 for occasional updates as well as running INLA
in verbose mode (if applicable), or 0 for no updates.
Tolerance for mean and variance of each data location.
Locations which do not meet these thresholds are masked out of the analysis.
Default: 1e-6 for both.
This function requires the INLA package, which is not a CRAN package.
See https://www.r-inla.org/download-install for easy installation instructions.